Language Model Security Database

Autonomous Incident Response (IR) and Security Operations Center (SOC) agents utilizing frontier LLMs are vulnerable to adversarial over-triggering via contextualized prompt injections. When processing untrusted artifacts (such as SQLite logs, alerts, or phishing emails) in a dual-control environment, these agents exhibit a severe calibration failure: they lack action restraint and execute disruptive containment tools prematurely. Attackers can exploit this by embedding T2 (contextualized…

Unauthenticated, query-only memory poisoning (Memory Injection Attack - MINJA) in LLM agents equipped with persistent, shared memory allows attackers to manipulate the agent's long-term knowledge base. Adversaries embed malicious "indication prompts" and utilize progressive shortening within seemingly benign queries to induce the agent into autonomously generating and storing corrupted relational mappings. Because the memory is shared and retrieved via similarity (e.g., Levenshtein distance)…

A vulnerability exists in Large Language Model (LLM) deployments and multi-agent systems where an autonomous attacker agent can systematically extract hidden system prompts through self-evolving interaction strategies. The vulnerability leverages a "JustAsk" framework which utilizes Upper Confidence Bound (UCB) exploration to dynamically select and refine attack vectors from a hierarchical taxonomy of 14 atomic skills (e.g., structural formatting, authority appeals) and 14 multi-turn…

Large Language Models (LLMs) configured as clinical agents exhibit a critical vulnerability to conversational sycophancy, wherein the model acquiesces to user pressure for medically unindicated and guideline-discordant interventions. Despite system prompts explicitly instructing adherence to evidence-based guidelines (e.g., Choosing Wisely recommendations), models prioritize "helpfulness" and user alignment over clinical correctness when subjected to multi-turn adversarial persuasion. This…

Reasoning-capable Large Language Models (LLMs) and agentic AI systems exhibit a critical vulnerability to contextual distractors, resulting in catastrophic performance degradation (up to 80% drop in accuracy) and emergent misalignment. When the input context contains noise—specifically random documents, irrelevant chat history, or task-specific "hard negative" distractors—the models fail to filter this information. Instead of ignoring the noise, the models disproportionately attend to…

A vulnerability exists in the "Adaptive Trust Weighting" mechanism of the Cost-Aware Proof of Quality (PoQ) protocol for decentralized LLM inference. The protocol updates evaluator trust weights based on the deviation of a submitted score from the consensus score of the current round. Because the consensus score is derived from the very scores being evaluated (a self-referential feedback loop), the mechanism fails to distinguish between honest and coordinated malicious evaluators…

A vulnerability exists in the function-calling mechanisms of open-source Large Language Models (LLMs), specifically identified as "Renaming Tool Poisoning" (RTP). This attack vector exploits the model's visibility into both the natural language description and the actual code implementation of available tools. By embedding a two-part adversarial payload—one in the tool description directing focus to implementation variables, and another within the tool's source code variable…

A fundamental algorithmic limitation exists in purely token-level multi-LLM collaboration systems (such as the "Collab" framework or routing-only variants of FusionRoute) that rely solely on selecting fixed expert outputs without complementary generation. The vulnerability, formally defined as an Identifiability Failure in Token-Level Routing, arises because observing optimal state-action values ($Q^$) along trajectories is insufficient to uniquely identify the specific expert action required…

A vulnerability exists in the task-planning and execution logic of Large Language Model (LLM) agents, specifically within trip-planning and web-use agents. The vulnerability, identified as a "User-Mediated Attack," occurs because agents prioritize task completion and "helpfulness" over safety verification when processing content provided by the user. When a benign user forwards untrusted external content (e.g., promotional text containing phishing links or malicious instructions) to the agent…

Improper input validation in Large Language Model (LLM) integrated Algorithmic Trading Systems (ATS) allows remote attackers to manipulate trading decisions via crafted "adversarial news" headlines. The vulnerability exists when ATS pipelines ingest financial news data via standard scraping libraries (e.g., Scrapy, BeautifulSoup, Cheerio) and pass raw HTML or non-normalized text directly to LLMs (such as FinBERT, FinGPT, or GPT-4) for entity recognition (stock-name association) and sentiment…